most citedConstrained Hybrid Metaheuristic: A Universal Framework for Continuous Optimisation

1 citations · 1 across the 3 of their papers we have counts for

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5 papers

cs.NE20261 cited

Constrained Hybrid Metaheuristic: A Universal Framework for Continuous Optimisation

Piotr A. Kowalski, Szymon Kucharczyk, Jacek Mańdziuk

This paper presents the constrained Hybrid Metaheuristic (cHM) algorithm as a general framework for continuous optimisation. Unlike many existing metaheuristics that are tailored t…

cs.AI2026

Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing

Maciej Świechowski, Adam Żychowski, Jacek Mańdziuk

This paper examines the reasoning capabilities of Large Language Models (LLMs) from a novel perspective, focusing on their ability to operate within formally specified, rule-govern…

cs.NE2026

Automatic Design of Optimization Test Problems with Large Language Models

Wojciech Achtelik, Hubert Guzowski, Maciej Smołka +1

The development of black-box optimization algorithms depends on the availability of benchmark suites that are both diverse and representative of real-world problem landscapes. Wide…

cs.LG2025

The Impact of Bootstrap Sampling Rate on Random Forest Performance in Regression Tasks

Michał Iwaniuk, Mateusz Jarosz, Bartłomiej Borycki +4

Random Forests (RFs) typically train each tree on a bootstrap sample of the same size as the training set, i.e., bootstrap rate (BR) equals 1.0. We systematically examine how varyi…

cs.SD2025

Training chord recognition models on artificially generated audio

Martyna Majchrzak, Jacek Mańdziuk

One of the challenging problems in Music Information Retrieval is the acquisition of enough non-copyrighted audio recordings for model training and evaluation. This study compares…